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Background: This study aims to test the effect of the 10 most common nonurological primary cancers (skin, rectal, colon, lymphoma, leukemia, pancreas, stomach, esophagus, liver, lung) on overall mortality (OM) after secondary prostate cancer (PCa). Material and Methods: Within the Surveillance, Epidemiology, and End Results (SEER) database, patients with 10 most common primary cancers and concomitant secondary PCa (diagnosed 2004–2016) were identified and were matched in 1:4 fashion (age, year at diagnosis, race/ethnicity, treatment type, TNM stage) with primary PCa controls. OM was compared between secondary and primary PCa patients and was stratified according to primary cancer type, as well as according to time interval between primary cancer vs. secondary PCa diagnoses. Results: We identified 24,848 secondary PCa patients (skin, n = 3,871; rectal, n = 798; colon, n = 3,665; lymphoma, n = 2,583; leukemia, n = 1,102; pancreatic, n = 118; stomach, n = 361; esophagus, n = 219; liver, n = 160; lung, n = 1,328) vs. 531,732 primary PCa patients. Secondary PCa characteristics were less favorable than those of primary PCa patients (PSA and grade), and smaller proportions of secondary PCa patients received active treatment. After 1:4 matching, all secondary PCa exhibited worse OM than primary PCa patients. Finally, subgroup analyses showed that the survival disadvantage of secondary PCa patients decreased with longer time interval since primary cancer diagnosis and subsequent secondary PCa. Conclusion: Patients with secondary PCa are diagnosed with less favorable PSA and grade. Even after matching for PCa characteristics, secondary PCa patients still exhibit worse survival. However, the survival disadvantage is attenuated, when secondary PCa diagnosis is made after longer time interval, since primary cancer diagnosis.
Comparative proteomics reveals a diagnostic signature for pulmonary head‐and‐neck cancer metastasis
(2018)
Patients with head‐and‐neck cancer can develop both lung metastasis and primary lung cancer during the course of their disease. Despite the clinical importance of discrimination, reliable diagnostic biomarkers are still lacking. Here, we have characterised a cohort of squamous cell lung (SQCLC) and head‐and‐neck (HNSCC) carcinomas by quantitative proteomics. In a training cohort, we quantified 4,957 proteins in 44 SQCLC and 30 HNSCC tumours. A total of 518 proteins were found to be differentially expressed between SQCLC and HNSCC, and some of these were identified as genetic dependencies in either of the two tumour types. Using supervised machine learning, we inferred a proteomic signature for the classification of squamous cell carcinomas as either SQCLC or HNSCC, with diagnostic accuracies of 90.5% and 86.8% in cross‐ and independent validations, respectively. Furthermore, application of this signature to a cohort of pulmonary squamous cell carcinomas of unknown origin leads to a significant prognostic separation. This study not only provides a diagnostic proteomic signature for classification of secondary lung tumours in HNSCC patients, but also represents a proteomic resource for HNSCC and SQCLC.